Fenglan Pang

507 total citations
9 papers, 232 citations indexed

About

Fenglan Pang is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Fenglan Pang has authored 9 papers receiving a total of 232 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Immunology and 2 papers in Cancer Research. Recurrent topics in Fenglan Pang's work include Single-cell and spatial transcriptomics (5 papers), Immune cells in cancer (4 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Fenglan Pang is often cited by papers focused on Single-cell and spatial transcriptomics (5 papers), Immune cells in cancer (4 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Fenglan Pang collaborates with scholars based in China and Russia. Fenglan Pang's co-authors include Meng Luo, Zhaochun Xu, Xiyun Jin, Pingping Wang, Chang Xu, Qinghua Jiang, Rui Cheng, Xiaoyu Lin, Lixing Xiao and Yideng Cai and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Fenglan Pang

9 papers receiving 231 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fenglan Pang China	7	204	41	35	34	19	9	232
Lixing Xiao China	7	190 0.9×	50 1.2×	40 1.1×	18 0.5×	20 1.1×	10	229
Chunshen Long China	11	246 1.2×	18 0.4×	48 1.4×	14 0.4×	6 0.3×	22	309
Jay S. Stanley United States	4	137 0.7×	24 0.6×	20 0.6×	44 1.3×	4 0.2×	6	175
Junchen Yang China	5	164 0.8×	29 0.7×	14 0.4×	28 0.8×	5 0.3×	8	219
Dongyuan Song United States	7	292 1.4×	36 0.9×	81 2.3×	36 1.1×	3 0.2×	16	330
Matthew Amodio United States	5	199 1.0×	40 1.0×	58 1.7×	67 2.0×	10 0.5×	12	268
Ruochen Jiang United States	2	160 0.8×	14 0.3×	38 1.1×	24 0.7×	5 0.3×	3	179
Ciyue Shen United States	5	135 0.7×	25 0.6×	18 0.5×	25 0.7×	21 1.1×	6	194
Dayanne M. Castro United States	4	222 1.1×	24 0.6×	14 0.4×	14 0.4×	4 0.2×	4	250
Mattias Rydenfelt Germany	7	314 1.5×	17 0.4×	26 0.7×	21 0.6×	8 0.4×	10	362

Countries citing papers authored by Fenglan Pang

Since Specialization

Citations

This map shows the geographic impact of Fenglan Pang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fenglan Pang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fenglan Pang more than expected).

Fields of papers citing papers by Fenglan Pang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fenglan Pang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fenglan Pang. The network helps show where Fenglan Pang may publish in the future.

Co-authorship network of co-authors of Fenglan Pang

This figure shows the co-authorship network connecting the top 25 collaborators of Fenglan Pang. A scholar is included among the top collaborators of Fenglan Pang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fenglan Pang. Fenglan Pang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Pang, Fenglan, Guangfu Xue, Yideng Cai, et al.. (2025). TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification. Briefings in Bioinformatics. 26(4). 1 indexed citations

2.

Xue, Guangfu, Yideng Cai, Xiyun Jin, et al.. (2024). Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm. Genome biology. 25(1). 241–241. 6 indexed citations

3.

Xiao, Lixing, Haoxiu Sun, Rui Cheng, et al.. (2024). Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor. Molecular Therapy — Nucleic Acids. 35(1). 102129–102129. 2 indexed citations

4.

Wang, Pingping, Shouping Xu, Tao Wang, et al.. (2024). Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network. Nature Communications. 15(1). 7101–7101. 22 indexed citations

5.

Yang, Wenyi, Pingping Wang, Meng Luo, et al.. (2023). DeepCCI: a deep learning framework for identifying cell–cell interactions from single-cell RNA sequencing data. Bioinformatics. 39(10). 18 indexed citations

6.

Yang, Wenyi, Meng Luo, Chang Xu, et al.. (2022). CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data. Briefings in Bioinformatics. 23(5). 7 indexed citations

7.

Xu, Chang, Xiyun Jin, Pingping Wang, et al.. (2022). DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Research. 50(22). e131–e131. 124 indexed citations

8.

Cheng, Rui, Lixing Xiao, Wenyang Zhou, et al.. (2021). A pan-cancer analysis of alternative splicing of splicing factors in 6904 patients. Oncogene. 40(35). 5441–5450. 17 indexed citations

9.

Jin, Xiyun, Wenyang Zhou, Meng Luo, et al.. (2021). Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing. Briefings in Bioinformatics. 22(6). 35 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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